1. Technical Field
The invention relates generally to communication systems; and, more particularly, it relates to signal processing within digital communication systems geared to compensate for channel induced deleterious effects.
2. Related Art
Signal processing within communication systems having a communication channel, in an effort to improve the quality of signals passing through the communication channel, have been under development for many years. In the past several years, emphasis has moved largely to the domain of digital communication systems that modulate bit streams into an analog signal for transmission over a communication channel. This channel can be a variety of channel types. Channel estimation, oftentimes performed to assist in calculating coefficients for equalizer taps to perform channel equalization, is a technique that is employed to try to minimize the deleterious effects that the communication channel may have on the transmitted data. For example, given a highly accurate estimate of the characteristics of the actual communication channel (achieved from channel estimation), then highly accurate channel equalization may be performed. This requires calculating a number of coefficients (the coefficients corresponding to the taps of the equalizer) to overcome the effects that the communication channel has on the received data. This attempts to try to generate a block that may be described as having a response that is the inverse of the channel response of the communication channel. Therefore, the data that are modulated and transmitted across the communication channel may be accurately recovered at the other end of the communication channel. The calculations required in performing channel estimation can prove to be quite computationally intensive using prior art approaches.
While there have been some approaches to try to address this problem in the prior art, they have been typically very computationally intensive. In some instance, a very large matrix is employed to try to describe the communication channel, and then this large matrix must then be inverted to try to equalize any channel effects that may undesirably affect a signal transmitted through it. This very intensive computation is typically employed within communication systems whose communication channels are static, or landline, where very little if no changes to the communication channel may occur. However, even within these systems, when changes do in fact occur, the communication system must typically characterize, or estimate the channel, and then try to equalize for any deleterious effects the communication channel may have on data that is transmitted through it. When the parameters of the communication channel change over time, the channel estimation and channel equalization must again be performed. When using prior art approaches, these processes are very computationally intensive, and the processing requirements of the system are typically very large. In the instance when the characteristics of the communication channel are changing over time, this can prove simply to be too burdensome for the system to accommodate. Most prior art approaches are simply unable to compensate for these rapidly changing effects within the communication channel.
Channel equalizers are essential building blocks of many communications systems. This is especially true in broadband applications where the inter-symbol-interference (ISI) is a critical problem that can significantly reduce the data throughput rate. In many such systems, the data are transmitted in packets. Each data packet usually will consist of a known training sequence followed by a portion of unknown data. The training sequence is usually employed to train the equalizer (by calculating the coefficients for the channel equalizer tap coefficients) to an optimal setting that is based on the communication channel's characteristics in its present state. Another approach employs the method described briefly above. In this situation, channel estimation is first employed to perform channel estimation on the communication channel between the transmitter and the receiver. This is typically performed by using the training sequence that is part of the transmitted data packet. After the channel estimation is performed, then the channel estimate is employed this estimate to compute the optimal equalizer tap coefficients to be able to compensate for any channel effects on transmitted data.
Further limitations and disadvantages of conventional and traditional systems will become apparent to one of skill in the art through comparison of such systems with the invention as set forth in the remainder of the present application with reference to the drawings.